Researchers propose TOOLDEC, a finite-state machine-guided decoding for LLMs, reducing errors and improving tool use.
Authors: Kexun Zhang, UC Santa Barbara and Equal contribution; Hongqiao Chen, Northwood High School and Equal contribution; Lei Li, Carnegie Mellon University; William Yang Wang,UC Santa Barbara. Table of Links Abstract and Intro Related Work ToolDec: LLM Tool Use via Finite-State Decoding Experiment: ToolDec Eliminates Syntax Errors Experiment: ToolDec Enables Generalizable Tool Selection Conclusion and References Appendix 6.
guided by a finite-state machine constructed from tool documentation and API signatures, accurately represents the grammar of tool calls, addressing prevalent issues like erroneous tool calls and poor generalization to unseen tools in existing models. Experiments demonstrate that TOOLDEC eliminates tool-related syntax errors, improves accuracy, and saves inference time across various benchmarks.
Tanmay Gupta and Aniruddha Kembhavi. Visual programming: Compositional visual reasoning without training. ArXiv, abs/2211.11559, 2022. Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. Retrieval augmented language model pre-training. In International conference on machine learning, pp. 3929–3938.
This paper presents Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. Guided open vocabulary image captioning with constrained beam search. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 936–945, Copenhagen, Denmark, September 2017. Association for Computational Linguistics. doi: 10.18653/v1/D17-1098. URL https://aclanthology.org/D17-1098.
United States Latest News, United States Headlines
Similar News:You can also read news stories similar to this one that we have collected from other news sources.
Syntax Error-Free and Generalizable Tool Use for LLMs: ToolDec Eliminates Syntax ErrorsResearchers propose TOOLDEC, a finite-state machine-guided decoding for LLMs, reducing errors and improving tool use.
Read more »
Syntax Error-Free and Generalizable Tool Use for LLMs: Related WorkResearchers propose TOOLDEC, a finite-state machine-guided decoding for LLMs, reducing errors and improving tool use.
Read more »
Syntax Error-Free and Generalizable Tool Use for LLMs: Abstract and IntroResearchers propose TOOLDEC, a finite-state machine-guided decoding for LLMs, reducing errors and improving tool use.
Read more »
Syntax Error-Free and Generalizable Tool Use for LLMs: ToolDec Enables Generalizable Tool SelectionResearchers propose TOOLDEC, a finite-state machine-guided decoding for LLMs, reducing errors and improving tool use.
Read more »
Can LLMs have a "dream-like" state to uniquely facilitate creativity?Explore the intriguing parallels between the hypnagogic state and AI creativity.
Read more »
Using LLMs to Correct Reasoning Mistakes: Related Works That You Should Know AboutThis paper explores few-shot in-context learning methods, which is typically used in realworld applications with API-based LLMs
Read more »